Bayesian Model Predictive Control for Quantum State Regulation under Decoherence
Thanana Nuchkrua
Abstract. We develop a Bayesian Model Predictive Control (BMPC) framework for adaptive quantum state regulation under model uncertainty. The method embeds Bayesian parameter inference directly into the receding-horizon optimization, enabling the controller to update uncertain Hamiltonian parameters online while computing constrained control inputs in real time. We formulate the BMPC architecture for Lindblad open-system dynamics and establish theoretical guarantees showing that posterior contraction drives the BMPC law toward the nominal MPC law, recovering its stability properties. Numerical experiments on single-qubit state-transfer tasks demonstrate that BMPC preserves high fidelity under parameter drift, decoherence, and measurement noise, and that short prediction horizons are sufficient for real-time feasibility — making BMPC a principled and practical strategy for quantum feedback control under uncertainty. Key Results
Theoretical Contributions
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